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Mitchell Porter wrote:

The idea is that a superintelligence would have a 'computational core' which

spends its time approximating Omega, and modules which take general

problems, encode them as halting problems, and look them up in Approximate

Omega.

Anders Sandberg wrote:

Ah, that's where the rub is: how do you convert a general problem into a

halting problem in an efficient way? <snip>

I would guess that the sum of work often remains constant in the general

case: the amount of work needed to encode a problem into a form solvable by

an algorithm and the amount of work in using the algorithm tend to be fairly

constant. Intelligence is about finding ways of getting around this by

exploiting patterns that make the problem non-general, such as in

mathematical tricks where a simple transformation makes a hard problem

simple.

D. T. Spreng, a swiss physicist, wrote (1) that to produce output Q, in a

time = T, using an energy = E, and some information = I

f (T) f (E) f (I) = C = constant.

In other words to carry out a given task, you can save information by using

more time and/or energy, or you can save time by using more energy and/or

information, or you can save time and information by using more energy. Now

wich is the goal of "take general problems, encode them as halting problems,

and look them up in Approximate Omega"? Save information, save time, or save

both?

(1): D. T. Spreng, ³On time, information and energy conservation²,

ORAU/IEAo78o22(R), Inst. For Energy Analysis, Oak Ridge Assoc. Universities,

Oak Ridge, Tennessee, 1978

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